Definition
Demand planning is the process of forecasting future customer demand for products to enable supply decisions — purchasing, production scheduling, and inventory positioning. It combines statistical forecasting methods (moving averages, exponential smoothing, regression) with market intelligence inputs (promotions, new product launches, seasonality, tariff signals). A demand plan is typically produced monthly or weekly, covers a rolling 3–18 month horizon, and is measured by forecast accuracy (typically expressed as 1 − MAPE, mean absolute percentage error). SMB distributors using spreadsheets achieve 60–70% forecast accuracy; AI-assisted tools typically achieve 80–92%.
Why It Matters
Every inventory decision downstream — how much safety stock to hold, when to reorder, how much to buy — is only as good as the demand forecast driving it. A 10-percentage-point improvement in forecast accuracy typically reduces excess inventory by 20–30% while simultaneously reducing stockout frequency. Demand planning is the highest-leverage analytical investment a distributor can make. Demand Forecaster →
Frequently Asked Questions
What is demand planning in supply chain management?
Demand planning is the process of forecasting how much of each product customers will buy over a future period. The output drives every supply decision: purchase orders, production schedules, and inventory positions. It integrates statistical methods with market intelligence to produce a consensus forecast.
What is the difference between demand planning and demand forecasting?
Demand forecasting is the statistical output — the predicted demand number. Demand planning is the broader process: gathering inputs, running forecasts, reviewing with sales and operations teams, incorporating qualitative adjustments, and publishing the final number that drives supply decisions. Forecasting is a component of planning.
How do you measure demand planning accuracy?
Forecast accuracy is commonly measured as 1 − MAPE (mean absolute percentage error). MAPE = average of |actual − forecast| / actual × 100. A MAPE of 20% means forecasts are on average 20% off. Industry benchmarks: spreadsheet users average 30–40% MAPE; best-in-class AI systems achieve 8–20% MAPE.